Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Modified Approach of Cluster Algorithm to Analysis Road Accident

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Reeta Bhardwaj, Ridhi, Rajeev Kumar

Reeta Bhardwaj, Ridhi and Rajeev Kumar. Modified Approach of Cluster Algorithm to Analysis Road Accident. International Journal of Computer Applications 166(2):24-28, May 2017. BibTeX

	author = {Reeta Bhardwaj and Ridhi and Rajeev Kumar},
	title = {Modified Approach of Cluster Algorithm to Analysis Road Accident},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {166},
	number = {2},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {24-28},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017913934},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Road accident is one of the crucial areas of research in India. A variety of research has been done on data collected through police records covering a limited portion of highways. The analysis of such data can only reveal information regarding that portion only; but accidents are scattered not only on highways but also on local roads. A different source of road accident data in India is Emergency Management Research Institute (EMRI) which serves and keeps track of every accident record on every type of road and cover information of entire State’s road accidents. In this paper, we have used data mining techniques to analyze the data provided by in which we first cluster the accident data and further association rule mining technique is applied to identify circumstances in which an accident may occur for each cluster. The results can be utilized to put some accident prevention efforts in the areas identified for different categories of accidents to overcome the number of accidents also the parameters of the proposed approach is compared with the existing approach on the basis of time and accuracy and proves that the proposed technique has better performance.


  1. R. Patel Nimisha, Sheetal Mehta “A Survey on Mining Algorithms” International Journal of Soft Computing and Engineering , vol. 2, issue 6, pp 460-463, January 2013.
  2. Sotiris Kotsiantis, DimitrisKanellopoulos, “Association Rules Mining: A Recent Overview” GESTS International Transactions on Computer Science and Engineering, vol.32 (1), pp 71-82, 2006.
  3. Rakesh Kumar Soni1, Neetesh Gupta, AmitSinhal, “An FP-Growth Approach to MiningAssociation Rules” International Journal of Computer Science and Mobile Computing, Vol. 2, Issue. 2, February 2013, pp 1 – 5.
  4. JaiWeiHan, Jian Pei, Yiwen Yin &Runying Mao, “Mining frequent patterns without candidate generation: A Frequent pattern tree approach” Data mining and knowledge discovery, Netherlands, pp 53-87, 2004.
  5. Huan Wu, Zhigang Lu, Lin Pan, RongSeng XU and Wenbaojiang “An improved Apriori based algorithm for association rule mining” IEEE Sixth international conference on fuzzy systems and knowledge discovery, pp 51-55, 2009.
  6. Badri Patel, Vijay K Chaudhari, Rajneesh K Karan, YK Rana “Optimization of Association Rule Mining Apriori Algorithm using ACO” International Journal of Soft Computing and Engineering vol 1, issue 1, pp 24-26, March 2011.
  7. K.Saravana Kumar, R.ManickaChezian,“A Survey on Association Rule Mining using Apriori Algorithm” International Journal of Computer Application, vol. 45, no. 5, pp 47-50, May 2012.
  8. Rafael S. Parpinelli, Heitor S. Lopes, Alex A. Freitas, “Data Mining With an Ant Colony Optimization Algorithm” IEEE Transactions on evolutionary computing, vol. 6, no. 4, pp 321-332, August 2002
  9. SuhaniNagpal “Improved Apriori Algorithm using logarithmic decoding and pruning” International Journal of Engineering Research and Applications, vol. 2, issue 3, pp. 2569-2572, May-Jun 2012.
  10. Fernando E. B. Otero, Alex A. Fretas and Colin G. Johnson “A new sequential covering strategy for inducing classification rules with ant colony algorithms” IEEE transaction on evolutionary computation, vol. 17, no. 1, pp 64-76, February 2013.
  11. Sang Jun Lee, KengSiau“A review of data mining techniques” Industrial Management and Data Systems, University of Nebraska-Lincoln Press, USA, pp41-46, 2001
  12. S. Shanthi, R.geethaRamani "feature relevance analysis and classfication of road traffic accident data through data mining techniques",2012pg no 24-26
  13. Dr. R. Geetha Ramani1, S. Shanthi2."Classifier Prediction Evaluation in Modeling Road Traffic Accident Data",IEEE International Conference on Computational Intelligence and Computing Research,pp 1-4,2012
  14. Global Burden of Disease Study 2013, Collaborators (22 August 2015). "Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.". Lancet (London, England) 386 (9995): 743–800.PMID 26063472
  15. GBD 2013 Mortality and Causes of Death, Collaborators (17 December 2014)."Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013."
  16. Global status report on road safety 2013: Supporting a decade of action (PDF) (in English and Russian). Geneva, Switzerland: world health organization WHO. 2013.ISBN 978 92 4 156456 4. Retrieved 3 October2014.
  17. Seoung-hun Park andYoung-guk Ha, "Large Imbalance Data Classification Based on MapReduce for Traffic Accident Prediction",Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing,pp45-49,2014
  18. EvangelosGakis, DionysiosKehagias and DimitriosTzovaras,"Mining Traffic Data for Road Incidents Detection",IEEE 17th international conference on Intelligent Transporatation System(ITSC) October 8-11,2014 Qingdao, Chine.
  19. An Shi,Zhang Tao, Zhang Xinming, Wang Jian ,"Evolution of Traffic Flow Analysis under Accidents on Highways Using Temporal Data Mining ", Fifth International conference On Intelligent Sytem Design And Engineering Apllication,2014, pp- 454-457
  20. A. T. Kashani, A. Shariat-Mohaymany, A. Ranjbari," A Data Mining Approach To Identify Key Factors Of Traffic Injury Severity", 2009 Traffic&Transportation, Vol. 23, 2011, No. 1, 11-17
  21. Suresh BabuChangalasetty, LalithaSarojaThota, Ahmed Said Badawy, Wade Ghribi," Classification of Moving Vehicles using K-modes Clustering",2015,pp 1-6
  22. RuiTian and Zhaosheng Yang and Maolei Zhang, "Method of Road Traffic Accidents causes Analysis based On data Minning", 2010, pp 1-4
  23. PannawatSriratna, PakornLeesutthipornchai,"Interesting-based Association Rules for Highway Traffic Data", 2015, pp1-6
  24. He song-bai ,wangYa-jun Sun yue-kun gao wen-weichenqiang An ya_qin "The research of multidimensional assocation rule in traffic Accident",2008. pg no 1-4.


Data Mining; Road Accidents; Association Rule Mining.